chr17.9913_chr17_22863184_22863669_-_0.R fitVsDatCorrelation=0.9493789141124 cont.fitVsDatCorrelation=0.319430420920642 fstatistic=6793.37115531992,36,324 cont.fstatistic=738.530677501544,36,324 residuals=-0.496628498127077,-0.0992081028806777,-0.00522428124205701,0.102130053272129,0.755852774667397 cont.residuals=-0.915554426925284,-0.448544710900934,-0.0474345351443247,0.397510144032589,1.50251294630121 predictedValues: Include Exclude Both chr17.9913_chr17_22863184_22863669_-_0.R.tl.Lung 219.592334691748 62.1580937907616 115.815852311341 chr17.9913_chr17_22863184_22863669_-_0.R.tl.cerebhem 98.9061031713647 97.695889862188 85.5742236460981 chr17.9913_chr17_22863184_22863669_-_0.R.tl.cortex 146.128372948704 64.4829661858239 78.6436647808545 chr17.9913_chr17_22863184_22863669_-_0.R.tl.heart 162.670254891302 60.7339407055873 157.103175166406 chr17.9913_chr17_22863184_22863669_-_0.R.tl.kidney 218.324363805477 65.7131021983881 101.552114452457 chr17.9913_chr17_22863184_22863669_-_0.R.tl.liver 177.202177020443 64.1597769839385 80.0086407410198 chr17.9913_chr17_22863184_22863669_-_0.R.tl.stomach 140.101608950513 69.3771851363773 90.6067139947114 chr17.9913_chr17_22863184_22863669_-_0.R.tl.testicle 324.678125644286 67.8841712447363 104.601239684824 diffExp=157.434240900986,1.21021330917665,81.6454067628797,101.936314185715,152.611261607089,113.042400036505,70.7244238141352,256.793954399549 diffExpScore=0.998932078271868 diffExp1.5=1,0,1,1,1,1,1,1 diffExp1.5Score=0.875 diffExp1.4=1,0,1,1,1,1,1,1 diffExp1.4Score=0.875 diffExp1.3=1,0,1,1,1,1,1,1 diffExp1.3Score=0.875 diffExp1.2=1,0,1,1,1,1,1,1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 96.9191886685125 104.621032383825 140.603654860389 cerebhem 98.7679792391644 106.869271853250 116.689633225016 cortex 101.904274019918 101.188189918514 90.280625676863 heart 92.586181523736 103.246175902733 104.171174882175 kidney 112.467905180175 145.977277502003 134.260793482846 liver 115.475071371534 92.5428145468737 124.791451917896 stomach 88.072075006233 101.679512153939 70.2238451042313 testicle 102.570095792520 100.222097362973 95.3102617771093 cont.diffExp=-7.70184371531302,-8.10129261408599,0.716084101404675,-10.6599943789975,-33.5093723218286,22.9322568246608,-13.6074371477056,2.34799842954693 cont.diffExpScore=2.04958623585182 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,0,0,0,0,0 cont.diffExp1.4Score=0 cont.diffExp1.3=0,0,0,0,0,0,0,0 cont.diffExp1.3Score=0 cont.diffExp1.2=0,0,0,0,-1,1,0,0 cont.diffExp1.2Score=2 tran.correlation=-0.450301954251095 cont.tran.correlation=0.313762040172565 tran.covariance=-0.0318942380755837 cont.tran.covariance=0.00333207999432442 tran.mean=127.488029201977 cont.tran.mean=104.069321401619 weightedLogRatios: wLogRatio Lung 6.00847423727615 cerebhem 0.0564851569130948 cortex 3.74309880261627 heart 4.53115043322563 kidney 5.74603926481243 liver 4.74364558971858 stomach 3.22657410485099 testicle 7.82565571815797 cont.weightedLogRatios: wLogRatio Lung -0.352674670716258 cerebhem -0.365168840605043 cortex 0.0325830505181606 heart -0.499397807666991 kidney -1.26559579845838 liver 1.02685600874421 stomach -0.65369859570384 testicle 0.106964800623979 varWeightedLogRatios=5.26942835540667 cont.varWeightedLogRatios=0.445419914494122 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.78664120727046 0.102123636935364 46.8710413270929 2.15002499148826e-146 *** df.mm.trans1 0.561477402939302 0.0864976536864362 6.49124431715479 3.19368018455168e-10 *** df.mm.trans2 -0.635109078286941 0.0864976536864362 -7.34250064850642 1.70787945159354e-12 *** df.mm.exp2 -0.0428060687350977 0.120458470395220 -0.355359557486098 0.722551471606813 df.mm.exp3 0.0165071311673918 0.120458470395220 0.137035868986485 0.891087557200686 df.mm.exp4 -0.628127276743672 0.120458470395220 -5.21447163227963 3.2928729640311e-07 *** df.mm.exp5 0.181255691942751 0.120458470395220 1.50471520473452 0.133371494005856 df.mm.exp6 0.187080780257818 0.120458470395220 1.55307285277666 0.121381952568545 df.mm.exp7 -0.0940547026138116 0.120458470395220 -0.780806051290717 0.435486672177608 df.mm.exp8 0.581029401447786 0.120458470395220 4.82348314353858 2.17473030432683e-06 *** df.mm.trans1:exp2 -0.754795787261653 0.104320095463276 -7.23538244390662 3.38094186842849e-12 *** df.mm.trans2:exp2 0.494984518517226 0.104320095463276 4.74486259161328 3.13421548516532e-06 *** df.mm.trans1:exp3 -0.423794432266572 0.104320095463276 -4.06244291077896 6.09739266351977e-05 *** df.mm.trans2:exp3 0.0202129281967417 0.104320095463277 0.193758720282779 0.846486227191205 df.mm.trans1:exp4 0.328079649192554 0.104320095463276 3.14493240957632 0.00181515936598753 ** df.mm.trans2:exp4 0.604948934074873 0.104320095463276 5.79896837122654 1.58514908627421e-08 *** df.mm.trans1:exp5 -0.187046630877337 0.104320095463276 -1.79300670735278 0.073904475905993 . df.mm.trans2:exp5 -0.125638401119021 0.104320095463277 -1.20435473684214 0.229331546293031 df.mm.trans1:exp6 -0.401562259744762 0.104320095463276 -3.84932795509302 0.000142694262874776 *** df.mm.trans2:exp6 -0.155385332059107 0.104320095463276 -1.48950527095527 0.137327123438255 df.mm.trans1:exp7 -0.355350163059455 0.104320095463276 -3.40634430481851 0.000741306305755527 *** df.mm.trans2:exp7 0.203931732228285 0.104320095463276 1.9548652761737 0.0514585788796046 . df.mm.trans1:exp8 -0.189967895754816 0.104320095463276 -1.82100960424917 0.069527590304779 . df.mm.trans2:exp8 -0.492907552111116 0.104320095463277 -4.72495303922179 3.43552838175468e-06 *** df.mm.trans1:probe2 0.209356789038841 0.0521600477316382 4.01373844816966 7.42935326894516e-05 *** df.mm.trans1:probe3 0.117513513529013 0.0521600477316382 2.25294106580607 0.0249314722828075 * df.mm.trans1:probe4 0.0150455623680425 0.0521600477316382 0.288449934813162 0.773186794608724 df.mm.trans1:probe5 -0.0235637473549124 0.0521600477316382 -0.451758546620723 0.651745197335943 df.mm.trans1:probe6 0.0745356197890295 0.0521600477316382 1.42897913308118 0.153973609386569 df.mm.trans2:probe2 -0.125787025823229 0.0521600477316382 -2.41155887108077 0.0164402618922836 * df.mm.trans2:probe3 0.151837042470598 0.0521600477316382 2.91098357984246 0.00385254968593393 ** df.mm.trans2:probe4 -0.118622001755545 0.0521600477316382 -2.27419273781826 0.0236079698694688 * df.mm.trans2:probe5 -0.093392352699881 0.0521600477316382 -1.79049592094665 0.0743077324832395 . df.mm.trans2:probe6 -0.0106954682871046 0.0521600477316382 -0.205050968168826 0.837661063888422 df.mm.trans3:probe2 0.287270833397519 0.0521600477316382 5.50748793167365 7.42312520069798e-08 *** df.mm.trans3:probe3 -0.170295080215672 0.0521600477316382 -3.26485667903977 0.00121225415373318 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.13649509584812 0.308064691617195 13.4273586308559 5.50608754007413e-33 *** df.mm.trans1 0.287861061184798 0.260927575712842 1.10322207378187 0.270749465028693 df.mm.trans2 0.486582703125632 0.260927575712842 1.86481900886217 0.0631106995522524 . df.mm.exp2 0.226584925101207 0.363373286034356 0.623559666628286 0.533355532084916 df.mm.exp3 0.459815893108175 0.363373286034356 1.26540918328460 0.206633934480556 df.mm.exp4 0.24094348154198 0.363373286034356 0.663074284220222 0.507754436529459 df.mm.exp5 0.52805758434155 0.363373286034356 1.45320970097847 0.147133382378110 df.mm.exp6 0.171804955619684 0.363373286034356 0.472805685565561 0.636670158381361 df.mm.exp7 0.570016258400424 0.363373286034356 1.56867959288160 0.117698690907571 df.mm.exp8 0.402520482790131 0.363373286034356 1.10773273176739 0.268798923112558 df.mm.trans1:exp2 -0.207688994414793 0.314690496762381 -0.659978602949731 0.509736441533367 df.mm.trans2:exp2 -0.205323201808599 0.314690496762381 -0.652460763578876 0.514566609324268 df.mm.trans1:exp3 -0.409659535249276 0.314690496762381 -1.30178553043057 0.193914487126224 df.mm.trans2:exp3 -0.493178449308005 0.314690496762381 -1.56718570907592 0.118047395738357 df.mm.trans1:exp4 -0.286681103397946 0.314690496762381 -0.910993837905486 0.362975878524339 df.mm.trans2:exp4 -0.254171893481625 0.314690496762381 -0.807688494239936 0.419862500415688 df.mm.trans1:exp5 -0.37926721545368 0.314690496762381 -1.20520708237357 0.229002872290640 df.mm.trans2:exp5 -0.194951214126522 0.314690496762381 -0.61950143436879 0.536021462851194 df.mm.trans1:exp6 0.00337219399882877 0.314690496762381 0.0107159066877545 0.991456702220204 df.mm.trans2:exp6 -0.294478164054963 0.314690496762381 -0.935770755979707 0.350088165510206 df.mm.trans1:exp7 -0.665738269753038 0.314690496762381 -2.11553344191302 0.0351472568899319 * df.mm.trans2:exp7 -0.598535035220586 0.314690496762381 -1.90198001330982 0.0580604413054421 . df.mm.trans1:exp8 -0.345851581314985 0.314690496762381 -1.09902137138934 0.272574717515328 df.mm.trans2:exp8 -0.445476391721723 0.314690496762381 -1.41560166673256 0.157852672893414 df.mm.trans1:probe2 0.411775319895225 0.157345248381191 2.61701782628759 0.00928604479078254 ** df.mm.trans1:probe3 0.12798974177497 0.157345248381191 0.813432519200689 0.416567480098139 df.mm.trans1:probe4 0.352534445522909 0.157345248381191 2.24051535810504 0.0257348464565376 * df.mm.trans1:probe5 0.170704188326468 0.157345248381191 1.08490208686133 0.27877160837808 df.mm.trans1:probe6 0.282688614088108 0.157345248381191 1.79661360604456 0.0733283203248022 . df.mm.trans2:probe2 0.206895358837626 0.157345248381191 1.31491329395848 0.189468949669879 df.mm.trans2:probe3 -0.0670244394840462 0.157345248381191 -0.425970534055596 0.670412152348026 df.mm.trans2:probe4 0.0547624326001249 0.157345248381191 0.348039951403269 0.728036192427335 df.mm.trans2:probe5 0.00130666142655668 0.157345248381191 0.00830442253579281 0.993379216457179 df.mm.trans2:probe6 0.0494612483549995 0.157345248381191 0.314348535236176 0.7534586561392 df.mm.trans3:probe2 0.1279652681983 0.157345248381191 0.813276978585883 0.416656502524598 df.mm.trans3:probe3 -0.0229469273390203 0.157345248381191 -0.145838069945577 0.884139911176025